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Robust change detection method with application in vibration structures monitoring

Robust change detection method with application in vibration...

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international conference on Emerging Technologies and Factory Automation (ETFA)

作者： T.D. Popescu National Institute for Research and Development in Informatics Bucharest Romania

ISBN: (纸本)0780372417

A signal processing technique for analysing both the transient natural excitations and the vibration structures response, capable of determining the time variations in the amplitude and in the frequency content of these structures, is presented. this technique makes use of an impulse invariant transformation for obtaining an equivalent model of the vibration structure, represented by a parallel-form realization of second-order subsystems, corresponding to different modes of vibration, having as input a stationary independent and identically distributed sequence. the method was applied for detection of changes in dynamic characteristics of a vibrating structure, a multi-story concrete building subject to an earthquake ground motion.

关键词： Robustness Monitoring Transient analysis Buildings Earthquakes Research and development Informatics Signal processing Signal analysis Frequency

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A 2.84W 16port Switch ASIC for high performance computing systems

A 2.84W 16port Switch ASIC for high performance computing sy...

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international conference on ASIC

作者： Hua Shen Ding-shan You Like Liu Jia Yang Xiao-xiao Jiang ASIC Group National Center of Intelligent Computing Systems (NCIC) Institute of Computing Technologies Chinese Academy and Sciences China NCIC China

the chip's structure, design trade-off, and physical implementation with power optimization of a 2.84 W switch ASIC, which is targeted for large scale parallel computing systems, are introduced in this paper. the chip supports not only multi-layer, multi-function packet switching with high throughput and low latency, but also provides advanced global barrier process accelerating between its 16 full-duplex ports. At 156.25 Mhz, the chip has 83.2 ns zero-load latency, 80 Gbps port-switching and 240 Gbps internal packet switching capacity with port's data throughput at 2 × 2.5 Gbps. the ASIC has been taped-out with 0.18 um/6metal CMOS technology, and has about 20 million transistors; 12.39 mm × 12.39 mm die size; with 1053 pin flip-chip package. the first pass silicon of this switch ASIC has successfully passed DFT, functional and system level testing.

关键词： Switches Application specific integrated circuits High performance computing Packet switching throughput Delay CMOS technology Design optimization Large-scale systems parallel processing

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Performance of Different Optimizers for Traffic Sign Classification

Performance of Different Optimizers for Traffic Sign Classif...

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international conference on Computing and Networking Technology (ICCNT)

作者： Ishita Joshi Milauni Desai Sannidhi Bookseller Rucha Mohod Chirag N. Paunwala Bhaumik Vaidya Sarvajanik College of Engineering and Technology Surat India

the use of traffic signs is vital especially when travelling in the highways or the hills in adverse environmental conditions. the pace of advancements in the field of Machine learning has opened doors for scopes of improvement in the performance of Convolutional Neural Network architectures dedicated to classification of traffic signs. Speed is as important as accuracy for such problems in the field of Advance Driver Assistance Systems and the use of GPU instead of CPU gives the benefit of parallel processing. Gradient Descent helps navigating towards the minima of the loss function. Purpose of various gradient descent optimizing algorithms is to help in quicker convergence. this proposed algorithm comprises of a compact Convolutional Neural Network architecture that was trained on GPU using RMSProp, Adam and Nadam optimizers on the BelgiumTS dataset. RMSProp and Adam caused either underfitting or over-fitting that was resolved by Nadam used with an appropriate dropout with 97.51 training accuracy and 96.78 testing accuracy. the predictions on test images convey that the architecture trained using Nadam works perfectly for blurry images, positionally challenging images and images with uneven illumination.

关键词： Training Road transportation Graphics processing units Computer architecture Convolutional neural networks Testing Vehicles

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Distributing Sparse Matrix/Graph Applications in Heterogeneous Clusters - an Experimental Study

Distributing Sparse Matrix/Graph Applications in Heterogeneo...

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international conference on High Performance Computing

作者： Charilaos Tzovas Maria Predari Henning Meyerhenke Humboldt-Universität zu Berlin Berlin Germany

Many problems in scientific and engineering applications contain sparse matrices or graphs as main input objects, e.g., numerical simulations on meshes. Large inputs are abundant these days and require parallel processing for memory size and speed. To optimize the execution of such simulations on cluster systems, the input problem needs to be distributed suitably onto the processing units (PUs). More and more frequently, such clusters contain different CPUs or a combination of CPUs and GPUs. this heterogeneity makes the load distribution problem quite challenging. Our study is motivated by the observation that established partitioning tools do not handle such heterogeneous distribution problems as well as homogeneous ones. In this paper, we first formulate the problem of balanced load distribution for heterogeneous architectures as a multiobjective, single-constraint optimization problem. We then split the problem into two phases and propose a greedy approach to determine optimal block sizes for each PU. these block sizes are then fed into numerous existing graph partitioners, for us to examine how well they handle the above problem. One of the tools we consider is an extension of our own previous work (von Looz et al., ICPP'18) called Geographer. Our experiments on well-known benchmark meshes indicate that only two tools under consideration are able to yield good quality. these two are ParMetis (both the geometric and the combinatorial variant) and Geographer. While ParMetis is faster, Geographer yields better quality on average.

关键词： Greedy algorithms High performance computing Memory management Tools parallel processing Numerical simulation Numerical models

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Access Point Management Using Java Based Microservice

Access Point Management Using Java Based Microservice

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international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Manoj Bhat Vishalakshi Prabhu H Dept of Computer Science and Engineering RV College of Engineering Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

this research paper explores the design and implementation of an event-driven monitoring system for Wi-Fi6 Access Points (APs) using Java-based microservices architecture. WiFi 6's primary advantage over other standards is that it provides maximum speed and accessibility in congested networks. the system aims to enhance network management capabilities by leveraging real-time event processing and Kafka messaging infrastructure. Key components include event listeners for capturing Quality of Experience (QoE) and Trap events via Kafka topics from APs, microservices for event processing, and Kafka topics for inter-service communication. the paper discusses the architecture, functionality, and integration of each component, as well as the system's ability to identify and handle various event types, such as ap-info and user reports. Additionally, the paper addresses error handling mechanisms and debugging practices for events with undefined syntax. Overall, the research contributes to the advancement of network monitoring solutions through the utilization of microservices and events-driven architectures.

关键词： Java Microservice architectures Computer architecture Syntactics Real-time systems Quality of experience Information technology Monitoring Wireless fidelity Standards

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A study of parallel perturbative gradient descent 94

A study of parallel perturbative gradient descent

引用

Proceedings of the 8th international conference on Neural Information processing Systems

作者： D. Lippe J. Alspector Dept. of EECS MIT Cambridge MA and Bellcore Morristown NJ Bellcore Morristown NJ

We have continued our study of a parallel perturbative learning method [Alspector et al., 1993] and implications for its implementation in analog VLSI. Our new results indicate that, in most cases, a single parallel perturbation (per pattern presentation) of the function parameters (weights in a neural network) is theoretically the best course. this is not true, however, for certain problems and may not generally be true when faced with issues of implementation such as limited precision. In these cases, multiple parallel perturbations may be best as indicated in our previous results.

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Training a neural network to observe the assembly mode of parallel robots

Training a neural network to observe the assembly mode of pa...

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Mechanical Engineering and Robotics Research (ICMERR), international conference on

作者： Adrián Peidró Antonio Manuel García-Pérez Paula Mollá-Santamaría Marc Fabregat-Jaén Óscar Reinoso Instituto de Investigación en Ingeniería de Elche (I3E) Miguel Hernández University Elche 03202 Spain Instituto de Investigación en Ingeniería de Elche (I3E) Valencian Graduate School and Research Network for AI

parallel robots have different solutions to their forward kinematic problem, i.e., their gripper can adopt different possible positions that are compatible with the displacements of the actuated joints. It is necessary to know the position of the gripper to control parallel robots in the operational space. However, usually, only these joint displacements are measured, which results in the indetermination of which of these possible positions is the one actually adopted by the gripper. Previous methods require redundant sensors to measure directly the position of the gripper or the displacements of passive joints. In this paper we propose to estimate the position of the gripper by solving the observability problem of parallel robots regarded as nonlinear dynamical systems. By taking the position coordinates of the gripper as states of the system, it is possible to reconstruct these states from the signals of inputs (control forces) and outputs (displacements of actuated joints) of the system. To that end, we propose a feedforward neural network to perform as the state observer, and we train it using thousands of trajectories. the proposed neural observer is tested on a simulated 2RPR parallel robot, and it is found that, combined with a refinement post-processing based on Newton-Raphson’s iterative method to solve the forward kinematics, the proposed method is able to exactly reconstruct the position of the gripper if the robot starts to move far from Type 2 singularities, at which different solutions merge.

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Radar Waveform Recognition Based on Time-frequency Images Under Deteriorating Channel Conditions

Radar Waveform Recognition Based on Time-frequency Images Un...

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international conference on Computer and Communications (ICCC)

作者： Lai Shen Daying Quan Xiaofeng Wang Xiaoping Jin Ning Jin dept. of college of information Engineering China Jiliang University Hangzhou China

In the process of electronic countermeasures, most studies of radar signal recognition focus on Additive White Gaussian Noise (AGWN) channel scenarios, while pay less attention to the influence of channel deterioration on radar signal recognition. therefore, we propose a low interception rate radar signal recognition method based on time-frequency images under channel impairment. First, we obtain shallow features of LPI radar signals in fading channels by Choi-Williams distribution (CWD) time-frequency transform that converts the signals to time-frequency images. Second, we design a parallel residual network model for feature learning and signal classification to realize the automatic modulation recognition of radar waveforms under channel fading conditions. Simulation results show that the proposed method effectively achieves the recognition of 12 kinds of radar waveforms both in AGWN channels and in multipath channels, which demonstrates the superiority of this model.

关键词： Time-frequency analysis Image recognition Computational modeling Modulation Radar Radar imaging Radar signal processing

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Integrating Speech Recognition with Predictive Text Models for Improved User Experience

Integrating Speech Recognition with Predictive Text Models f...

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conference on Information and Communication Technology (CICT)

作者： Hatesh Shyan Chandra Milan Dubey Mansha Rani Shyam Kaushik Uttam Kumar Department of CSE Chandigarh University Mohali India

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

Advances of speech recognition technology have dramatically altered human interaction with machines, making it more intuitive and efficient. the paper explores a new methodology in enhancing user experience by fusing spoken input with natural language processing or predictive text models. Taking a stride forward in using algorithms of natural language processing at their apex, we design a system that predicts the next word with accuracy through spoken input. Our methodology involves training a hybrid model that combines classic n-gram techniques with modern deep learning architectures such as Long Short-Term Memory (LSTM) networks so as to better understand context and achieve higher accuracy in predictions. the performance of our proposed model by using different metrics, namely accuracy, response time, and user satisfaction, using a variety of datasets containing diverse speech patterns and linguistic contexts. Our experimental results showed improvements of significant orders in prediction accuracy and lower reaction times compared to competing systems. Feedback from the users further showed improvements of significant orders in interaction fluidity and satisfaction. Indeed, this work opens new avenues for integrating speech recognition with predictive models for text in an application for user-centric applications.

关键词： Analytical models Accuracy Shape Noise Speech recognition Predictive models User experience Natural language processing Long short term memory Context modeling

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Stochastic Computation Based Hybrid Artificial Neural Networks and Spike Neural Network

Stochastic Computation Based Hybrid Artificial Neural Networ...

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international conference on Computer and Communications (ICCC)

作者： Yakun Zhou Jienan Chen Yizhuo Zhou Ziyang Shao National Key Laboratory of Science and Technology on Communication University of Electronic Science and Technology of China Chengdu Sichuan China

Neuromorphic computing is a promising technology for future machine learning research, which is envisioned to provide lower power consumption, fault tolerance, and massive parallelism. However, the neuromorphic network, i.e., spike neural network (SNN), suffers from a broader range of its application compared with artificial neural networks (ANN). In this work, we propose a stochastic computation SC-based hybrid ANN and SNN scheme, which supports both ANN and SNN computation in SC domain. the ANN computation is converted to SC, which is implemented with simple logic gates. Moreover, we propose a new two-stage stochastic computation that is compatible with the sequence computation style of SNN. Consequently, the logic resource for the hybrid SNN and ANN can be shared. Compared to the state-of-the-art hybrid scheme, the proposed SC-based ANN and SNN exhibit much lower hardware costs and higher hardware efficiency.

关键词： Fault tolerance Power demand Neuromorphic engineering Fault tolerant systems Artificial neural networks Machine learning parallel processing

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